新一代植物表型组学的发展之路

doi:10.11983/CBB19141

摘要/Abstract

摘要： 随着多种植物全基因组测序的完成, 科研人员越来越认识到植物表型研究的重要性, 并将其提升至“组学”的高度。植物表型组学是研究植物生长、表现和组成的科学, 能够有效追踪基因型、环境因素和表型之间的联系, 是突破未来作物学研究和应用的关键领域。该文介绍了植物表型采集分析经历的从手工测量计数的初始阶段到特定测量工具的辅助阶段再到高通量表型组学3个阶段; 提出了推动植物表型采集分析发展的3个要素: 表型组学研究设施、表型采集技术及图像数据分析方法; 进而详细阐述了表型组学设施的发展、国际上代表性的设施平台情况以及表型采集传感器和图像数据分析方法的发展, 并展望了植物表型组学未来的研究方向。

关键词: 植物表型组学, 表型研究设施, 采集技术, 图像分析

Abstract: Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant research, promising to alleviate the phenotypic bottleneck. Plant phenomics is a science that studies the growth, performance and composition of plants. It can effectively track the relationship among genotypes, environmental factors, and phenotypes. It is a key research field to break through the future crop research and application. In this paper, three stages of plant phenotypic analysis are discussed, that is, from the initial stage of manual measurement and counting and the assistant stage of specific measurement tools to the stage of high throughput phenomics. It is proposed that the development of plant phenotypic acquisition and analysis is driven by three important factors: phenotypic research facilities, phenotype acquisition technology and image analysis methods. Finally, the plant phenomic research is prospected.

Key words: plant phenomics, phenotypic research facilities, acquisition technology, image analysis

胡伟娟,傅向东,陈凡,杨维才. 新一代植物表型组学的发展之路. 植物学报, 2019, 54(5): 558-568.

Weijuan Hu,Xiangdong Fu,Fan Chen,Weicai Yang. A Path to Next Generation of Plant Phenomics. Chinese Bulletin of Botany, 2019, 54(5): 558-568.

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链接本文: https://www.chinbullbotany.com/CN/10.11983/CBB19141

https://www.chinbullbotany.com/CN/Y2019/V54/I5/558

图/表 2

参考文献 77

1	周济, Tardieu F, Pridmore T, Doonan J, Reynolds D, Hall N, Griffiths S, 程涛, 朱艳, 王秀娥, 姜东, 丁艳锋 (2018). 植物表型组学: 发展、现状与挑战. 南京农业大学学报 41, 580-588.
2	Albetis J, Jacquin A, Goulard M, Poilvé H, Rousseau J, Clenet H, Dedieu G, Duthoit S (2019). On the potentiality of UAV multispectral imagery to detect Flavescence dorée and grapevine trunk diseases. Remote Sens 11, 23.
3	Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE (2018). Translating high-throughput phenotyping into genetic gain. Trends Plant Sci 23, 451-466.
4	Araus JL, Serret MD, Edmeades GO (2012). Phenotyping maize for adaptation to drought. Front Physiol 3, 305.
5	Awada L, Phillips PWB, Smyth SJ (2018). The adoption of automated phenotyping by plant breeders. Euphytica 214, 148.
6	Bao Y, Tang L, Srinivasan S, Schnable PS (2019). Field- based architectural traits characterisation of maize plant using time-of-flight 3D imaging. Biosyst Eng 178, 86-101.
7	Basnayake J, Lakshmanan P, Jackson P, Chapman S, Natarajan S (2017). Canopy temperature: a predictor of sugarcane yield for irrigated and rainfed conditions. Int Sugar J 29, 1-9.
8	Bauer SD, Korč F, Förstner W (2011). The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precis Agric 12, 361-377.
9	Bowman BC, Chen J, Zhang J, Wheeler J, Wang Y, Zhao W, Nayak S, Heslot N, Bockelman H, Bonman JM (2015). Evaluating grain yield in spring wheat with canopy spectral reflectance. Crop Sci 55, 1881-1890.
10	Camino C, González-Dugo V, Hernández P, Sillero JC, Zarco-Tejada PJ (2018). Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture. Int J Appl Earth Obs Geoinform 70, 105-117.
11	Casanova JJ, O'Shaughnessy SA, Evett SR, Rush CM (2014). Development of a wireless computer vision instrument to detect biotic stress in wheat. Sensors 14, 17753-17769.
12	Chaerle L, Hulsen K, Hermans C, Strasser RJ, Valcke R, Höfte M, Van Der Straeten D (2003). Robotized time- lapse imaging to assess in-planta uptake of phenylurea herbicides and their microbial degradation. Physiol Plantarum 118, 613-619.
13	Chaerle L, Lenk S, Leinonen I, Jones HG, Van Der Straeten D, Buschmann C (2009). Multi-sensor plant imaging: towards the development of a stress-catalogue. Biotechnol J 4, 1152-1167.
14	Chen DJ, Neumann K, Friedel S, Kilian B, Chen M, Altmann T, Klukas C (2014). Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. Plant Cell 26, 4636-4655.
15	Cobb JN, DeClerck G, Greenberg A, Clark R, McCouch S (2013). Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype phenotype relationships and its relevance to crop improvement. Theor Appl Genet 126, 867-887.
16	Douarre C, Schielein R, Frindel C, Gerth S, Rousseau D (2018). Transfer learning from synthetic data applied to soil-root segmentation in X-ray tomography images. J Imaging 4, 65.
17	El-Hendawy S, Al-Suhaibani N, Dewir YH, Elsayed S, Alotaibi M, Hassan W, Refay Y, Tahir MU (2019). Ability of modified spectral reflectance indices for estimating growth and photosynthetic efficiency of wheat under saline field conditions. Agronomy 9, 35.
18	Elsayed S, Barmeier G, Schmidhalter U (2018). Passive reflectance sensing and digital image analysis allows for assessing the biomass and nitrogen status of wheat in early and late tillering stages. Front Plant Sci 9, 1478.
19	Elsayed S, Mistele B, Schmidhalter U (2011). Can changes in leaf water potential be assessed spectrally? Funct Plant Biol 38, 523-533.
20	Enders TA, St Dennis S, Oakland J, Callen ST, Gehan MA, Miller ND, Spalding EP, Springer NM, Hirsch CD (2019). Classifying cold-stress responses of inbred maize seedlings using RGB imaging. Plant Direct 3, 1-11.
21	Fabre J, Dauzat M, Negre V, Wuyts N, Tireau A, Gennari E, Neveu P, Tisne S, Massonnet C, Hummel I, Granier C (2011). PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context. BMC Plant Biol 11, 77.
22	Fahlgren N, Gehan MA, Baxter I (2015). Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 24, 93-99.
23	Finkel E (2009). With 'Phenomics', plant scientists hope to shift breeding into overdrive. Science 325, 380-381.
24	Fiorani F, Schurr U (2013). Future scenarios for plant phenotyping. Annu Rev Plant Biol 64, 267-291.
25	Fischer RA, Rees D, Sayre KD, Lu ZM, Condon AG, Saavedra AL (1998). Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies. Crop Sci 38, 1467-1475.
26	Franceschini MHD, Bartholomeus H, van Apeldoorn DF, Suomalainen J, Kooistra L (2019). Feasibility of unmanned aerial vehicle optical imagery for early detection and severity assessment of late blight in potato. Remote Sens 11, 224.
27	Gomez FE, Carvalho Jr G, Shi FH, Muliana AH, Rooney WL (2018). High throughput phenotyping of morpho- anatomical stem properties using X-ray computed tomography in sorghum. Plant Methods 14, 59.
28	Gutiérrez S, Tardaguila J, Fernández-Novales J, Diago MP (2019). On-the-go hyperspectral imaging for the in-field estimation of grape berry soluble solids and anthocyanin concentration. Aust J Grape Wine Res 25, 127-133.
29	Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F (2011). HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinform 12, 148.
30	Hatfield R, Fukushima RS (2005). Can lignin be accurately measured? Crop Sci 45, 832-839.
31	Jansen M, Gilmer F, Biskup B, Nagel KA, Rascher U, Fischbach A, Briem S, Dreissen G, Tittmann S, Braun S, De Jaeger I, Metzlaff M, Schurr U, Scharr H, Walter A (2009). Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Funct Plant Biol 36, 902-914.
32	Klukas C, Pape JM, Entzian A (2012). Analysis of high- throughput plant image data with the information system IAP. J Integr Bioinform 9, 191.
33	Li B, Xu XM, Han JW, Zhang L, Bian CS, Jin LP, Liu JG (2019). The estimation of crop emergence in potatoes by UAV RGB imagery. Plant Methods 15, 15.
34	Lobet G (2017). Image analysis in plant sciences: publish then perish. Trends Plant Sci 22, 559-566.
35	Malambo L, Popescu SC, Horne DW, Pugh NA, Rooney WL (2019). Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial lidar data. ISPRS J Photogramm 149, 1-13.
36	Merlot S, Mustilli AC, Genty B, North H, Lefebvre V, Sotta B, Vavasseur A, Giraudat J (2002). Use of infrared thermal imaging to isolate Arabidopsis mutants defective in stomatal regulation. Plant J 30, 601-609.
37	Mir RR, Reynolds M, Pinto F, Khan MA, Bhat MA (2019). High-throughput phenotyping for crop improvement in the genomics era. Plant Sci 282, 60-72.
38	Moghimi A, Yang C, Miller ME, Kianian SF, Marchetto PM (2018). A novel approach to assess salt stress tolerance in wheat using hyperspectral imaging. Front Plant Sci 9, 1182.
39	Munns R, James RA, Sirault XRR, Furbank RT, Jones HG (2010). New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J Exp Bot 61, 3499-3507.
40	Murchie EH, Kefauver S, Araus JL, Muller O, Rascher U, Flood PJ, Lawson T (2018). Measuring the dynamic photosynthome. Ann Bot 122, 207-220.
41	Neilson EH, Edwards AM, Blomstedt CK, Berger B, Møller BL, Gleadow RM (2015). Utilization of a high- throughput shoot imaging system to examine the dynamic phenotypic responses of a C₄ cereal crop plant to nitrogen and water deficiency over time. J Exp Bot 66, 1817-1832.
42	Panjvani K, Dinh AV, Wahid KA (2019). LiDARPheno—a low-cost LiDAR-based 3D scanning system for leaf morphological trait extraction. Front Plant Sci 10, 147.
43	Patil JK, Kumar R (2017). Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Eng Agric, Environ Food 10, 69-78.
44	Poorter H, Bühler J, van Dusschoten D, Climent J, Postma JA (2012). Pot size matters: a meta-analysis of the effects of rooting volume on plant growth. Funct Plant Biol 39, 839-850.
45	Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH, Pridmore TP, French AP (2017). Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience 6, gix083.
46	Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH, Pridmore TP, French AP (2018). Erratum to: deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience 7, 042.
47	Pound MP, French AP, Murchie EH, Pridmore TP (2014). Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol 166, 1688-1698.
48	Rascher U, Blossfeld S, Fiorani F, Jahnke S, Jansen M, Kuhn AJ, Matsubara S, Märtin LLA, Merchant A, Metzner R, Müller-Linow M, Nagel KA, Pieruschka R, Pinto F, Schreiber CM, Temperton VM, Thorpe MR, van Dusschoten D, van Volkenburgh E, Windt CW, Schurr U (2011). Non-invasive approaches for phenotyping of enhanced performance traits in bean. Funct Plant Biol 38, 968-983.
49	Raza SEA, Prince G, Clarkson JP, Rajpoot NM (2015). Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS One 10, e0123262.
50	Raza SEA, Smith HK, Clarkson GJJ, Taylor G, Thompson AJ, Clarkson J, Rajpoot NM (2014). Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS One 9, e97612.
51	Reynolds MP, Rajaram S, Sayre KD (1999). Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand. Crop Sci 39, 1611-1621.
52	Ribaut JM, de Vicente MC, Delannay X (2010). Molecular breeding in developing countries: challenges and perspectives. Curr Opin Plant Biol 13, 213-218.
53	Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F, Ober ES (2019). Review: new sensors and data-driven approaches—a path to next generation phenomics. Plant Sci 282, 2-10.
54	Schreiber U (2004). Pulse-Amplitude-Modulation (PAM) fluorometry and saturation pulse method: an overview. In: Papageorgiou GC, Govindjee, eds. Chlorophyll Fluorescence: A Signature of Photosynthesis. Dordrecht: Springer. pp. 279-319.
55	Seelig HD, Hoehn A, Stodieck LS, Klaus DM, Adams III WW, Emery WJ (2008). The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared. Int J Remote Sens 29, 3701-3713.
56	Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2016). Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21, 110-124.
57	Subedi P, Walsh K, Purdy P (2013). Determination of optimum maturity stages of mangoes using fruit spectral signatures. Acta Hortic 992, 521-527.
58	Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M (2017). Plant phenomics, from sensors to knowledge. Curr Biol 27, R770-R783.
59	Tester M, Langridge P (2010). Breeding technologies to increase crop production in a changing world. Science 327, 818-822.
60	Thorp KR, Thompson AL, Harders SJ, French AN, Ward RW (2018). High-throughput phenotyping of crop water use efficiency via multispectral drone imagery and a daily soil water balance model. Remote Sens 10, 1682.
61	Tripodi P, Massa D, Venezia A, Cardi T (2018). Sensing technologies for precision phenotyping in vegetable crops: current status and future challenges. Agronomy 8, 57.
62	Tuberosa R (2011). Phenotyping drought-stressed crops: key concepts, issues and approaches. In: Monneveux P, Ribaut JM, eds. Drought Phenotyping in Crops: From Theory to Practice. Texcoco: CGIAR Generation Challenge Programme. pp. 3-35.
63	van Veelen A, Tourell MC, Koebernick N, Pileio G, Roose T (2018). Correlative visualization of root mucilage degradation using X-ray CT and MRI. Front Environ Sci 6, 32.
64	Vasseur F, Wang G, Bresson J, Schwab R, Weigel D (2017). Image-based methods for phenotyping growth dynamics and fitness in large plant populations. BioRxiv doi: .
65	Veys C, Chatziavgerinos F, AlSuwaidi A, Hibbert J, Hansen M, Bernotas G, Smith M, Yin HJ, Rolfe S, Grieve B (2019). Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape. Plant Methods 15, 4.
66	Wang X, Xuan H, Evers B (2019). High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat. BioRxiv doi: .
67	Ward B, Brien C, Oakey H, Pearson A, Negrão S, Schilling RK, Taylor J, Jarvis D, Timmins A, Roy SJ, Tester M, Berger B, van den Hengel A (2019). High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare). Plant J 98, 555-570.
68	White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA, Kimball BA, Roth RL, Strand RJ, Thorp KR, Wall GW, Wang GY (2012). Field-based phenomics for plant genetics research. Field Crops Res 133, 101-112.
69	Wiley E, Casper BB, Helliker BR (2016). Recovery following defoliation involves shifts in allocation that favor storage and reproduction over radial growth in black oak. J Ecol 10, 1365-2745.
70	Woo NS, Badger MR, Pogson BJ (2008). A rapid, non- invasive procedure for quantitative assessment of drought survival using chlorophyll fluorescence. Plant Methods 4, 27.
71	Xu T, Su CL, Hu D, Li FF, Lu QQ, Zhang TT, Xu QS (2016). Molecular distribution and toxicity assessment of praseodymium by Spirodela polyrrhiza. J Hazard Mater 312, 132-140.
72	Xu Z, Valdes C, Clarke J (2018). Existing and potential statistical and computational approaches for the analysis of 3D CT images of plant roots. Agronomy 8, 71.
73	Yang WN, Guo ZL, Huang CL, Duan LF, Chen GX, Jiang N, Fang W, Feng H, Xie WB, Lian XM, Wang GW, Luo QM, Zhang QF, Liu Q, Xiong LZ (2014). Combining high- throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5, 5087.
74	Yao JN, Sun DW, Cen HY, Xu HX, Weng HY, Yuan F, He Y (2018). Phenotyping of Arabidopsis drought stress response using kinetic chlorophyll fluorescence and multicolor fluorescence imaging. Front Plant Sci 9, 603.
75	Zhang Y, Du JJ, Wang JL, Ma LM, Lu XJ, Pan XD, Guo XY, Zhao CJ (2018). High-throughput micro-phenotyping measurements applied to assess stalk lodging in maize (Zea mays L.). Biol Res 51, 40.
76	Zhao ZQ, Ma LH, Cheung YM, Wu XD, Tang YY, Chen CLP (2015). ApLeaf: an efficient android-based plant leaf identification system. Neurocomputing 151, 1112-1119.
77	Zhou W, Sui ZH, Wang JG, Hu YY, Kang KH, Hong HR, Niaz Z, Wei HH, Du QW, Peng C, Mi P, Que Z (2016). Effects of sodium bicarbonate concentration on growth, photosynthesis, and carbonic anhydrase activity of macroalgae Gracilariopsis lemaneiformis, Gracilaria vermiculophylla, and Gracilaria chouae( Gracilariales, Rhodophyta). Photosynth Res 128, 259-270.

分类	采集方式	国际代表性平台
分类	采集方式	平台名称	国家	概况/特色
温室型表型平台(可控环境)	传送式	植物表型加速器	澳大利亚	主要有2套大型温室表型平台, 合计通量2400盆。主要应用于非生物胁迫和植物衰老等方面
		德国Julich中心表型平台	德国	自主研发了温室表型系统, 包括根系MRI扫描系统和根系PET-CT扫描系统, 用于植物地上及地下部位的表型研究
		英国亚伯大学国家植物表型中心	英国	一套通量800盆的温室表型系统。重点关注能源植物(草本)研究
		法国农科院表型中心	法国	在蒙彼利埃(Montpelier)和第戎(Dijon)各有一套大型温室表型平台, 合计通量2800盆。用于多种农作物的育种
		根特大学温室表型平台	比利时	主要集成可见光、热成像及高光谱成像等成像单元, 主要应用于玉米等农作物的非生物胁迫研究
	轨道式	德国马普学会轨道型温室表型平台	德国	轨道上搭载了多光谱激光3D成像单元, 用于各种作物的三维结构采集及光谱成像
	轨道式	上海师范大学轨道型温室表型平台	中国	搭载了RGB、多光谱和多光谱激光3D成像传感器, 用于采集分析各种植物的颜色、三维结构、植物反射指数及光谱成像
田间表型平台	轨道式	英国洛桑实验站田间表型平台	英国	成像覆盖面积10 m×120 m, 包括可见光、红外、激光3D、叶绿素荧光、高光谱、NDVI和CO₂等多个传感器。应用于油菜和小麦等作物不同营养处理下相关田间表型研究
	轨道式	英国JIC田间表型平台	英国	以植物激光三维扫描测量仪为核心, 通过3D顶部成像, 获取植物生长情况。对生长在自然土壤里的农作物进行高通量表型测量
	绳索悬浮式	美国内布拉斯加林肯大学田间表型平台	美国	高吞吐量的表型机器人安装在一个30 t重的钢架上, 沿着200英尺高的钢轨移动, 在1.5英亩田间移动。主要应用于研究植物高度、叶表面积、生物量、耐热性和对当地条件的其它反应的众多变化
	行走式(手动或自动)	澳大利亚昆士兰大学和CSIRO	澳大利亚	行走式田间表型, 三轮带电动驱动系统, 配置可见光和激光扫描测量器。对油菜等田间作物进行高通量表型测量
		日本东京大学田间表型平台	日本	在集成多个低成本传感器的基础上, 通过网络云服务实现对植物在不同环境下的长期观察
		英国诺维奇科学研究院作物表型监测平台Crop Quant	英国	通过自主研发的软件系统动态控制, 根据不同光照条件自动调整成像模式对田间作物的连续拍摄, 完成初步的表型分析, 实现对作物全生育期关键性状的高通量、高频率表型分析
	无人机(UAV)	德国波恩大学田间平台	德国	利用无人机搭载不同类型的高光谱传感器, 主要应用于监测田间大麦的表型参数
		CSIRO昆士兰生物科学区	澳大利亚	改装后的载人直升机Pheno-Copter被应用于测量数以千计的田间小区的冠层温度和倒伏情况
		国际玉米和小麦改良中心CIMMYT	意大利	结合全球定位系统和无人机影像信息来创建精确的正射影像图, 用于计算并分析植物覆盖率和光合作用

分类	采集方式	国际代表性平台
分类	采集方式	平台名称	国家	概况/特色
温室型表型平台(可控环境)	传送式	植物表型加速器	澳大利亚	主要有2套大型温室表型平台, 合计通量2400盆。主要应用于非生物胁迫和植物衰老等方面
		德国Julich中心表型平台	德国	自主研发了温室表型系统, 包括根系MRI扫描系统和根系PET-CT扫描系统, 用于植物地上及地下部位的表型研究
		英国亚伯大学国家植物表型中心	英国	一套通量800盆的温室表型系统。重点关注能源植物(草本)研究
		法国农科院表型中心	法国	在蒙彼利埃(Montpelier)和第戎(Dijon)各有一套大型温室表型平台, 合计通量2800盆。用于多种农作物的育种
		根特大学温室表型平台	比利时	主要集成可见光、热成像及高光谱成像等成像单元, 主要应用于玉米等农作物的非生物胁迫研究
	轨道式	德国马普学会轨道型温室表型平台	德国	轨道上搭载了多光谱激光3D成像单元, 用于各种作物的三维结构采集及光谱成像
	轨道式	上海师范大学轨道型温室表型平台	中国	搭载了RGB、多光谱和多光谱激光3D成像传感器, 用于采集分析各种植物的颜色、三维结构、植物反射指数及光谱成像
田间表型平台	轨道式	英国洛桑实验站田间表型平台	英国	成像覆盖面积10 m×120 m, 包括可见光、红外、激光3D、叶绿素荧光、高光谱、NDVI和CO₂等多个传感器。应用于油菜和小麦等作物不同营养处理下相关田间表型研究
	轨道式	英国JIC田间表型平台	英国	以植物激光三维扫描测量仪为核心, 通过3D顶部成像, 获取植物生长情况。对生长在自然土壤里的农作物进行高通量表型测量
	绳索悬浮式	美国内布拉斯加林肯大学田间表型平台	美国	高吞吐量的表型机器人安装在一个30 t重的钢架上, 沿着200英尺高的钢轨移动, 在1.5英亩田间移动。主要应用于研究植物高度、叶表面积、生物量、耐热性和对当地条件的其它反应的众多变化
	行走式(手动或自动)	澳大利亚昆士兰大学和CSIRO	澳大利亚	行走式田间表型, 三轮带电动驱动系统, 配置可见光和激光扫描测量器。对油菜等田间作物进行高通量表型测量
		日本东京大学田间表型平台	日本	在集成多个低成本传感器的基础上, 通过网络云服务实现对植物在不同环境下的长期观察
		英国诺维奇科学研究院作物表型监测平台Crop Quant	英国	通过自主研发的软件系统动态控制, 根据不同光照条件自动调整成像模式对田间作物的连续拍摄, 完成初步的表型分析, 实现对作物全生育期关键性状的高通量、高频率表型分析
	无人机(UAV)	德国波恩大学田间平台	德国	利用无人机搭载不同类型的高光谱传感器, 主要应用于监测田间大麦的表型参数
		CSIRO昆士兰生物科学区	澳大利亚	改装后的载人直升机Pheno-Copter被应用于测量数以千计的田间小区的冠层温度和倒伏情况
		国际玉米和小麦改良中心CIMMYT	意大利	结合全球定位系统和无人机影像信息来创建精确的正射影像图, 用于计算并分析植物覆盖率和光合作用

分类		成像技术	元数据	波长范围	采集性状 (传统农艺性状)		新参数		应用实例
二维成像技术		可见光成像	灰度或彩色图像, RGB通道反射值	400-700 nm	株高, 叶面积, 物候学信息, 叶型, 根系结构, 产量性状, 穗型, 种子形态, 绝对生长率(GR)和相对生长率(RGR)等		投影面积, 紧密度, 叶片衰老指数、伸长速率、卷曲指数, 叶面积垂直分布, 绿度, 开花率等		玉米耐冷性(Enders et al., 2019), 小麦产量预测(Bowman et al., 2015), 马铃薯出苗量预测(Li et al., 2019), 小麦开花率(Wang et al., 2019), 大麦叶片伸长QTL (Ward et al., 2019), 拟南芥群体生长(Vasseur et al., 2017), 春小麦产量性状(Neilson et al., 2015), 大麦抗旱性状(Chen et al., 2014), 水稻QTL (Yang et al., 2013), C₄作物地上部生物量(Fiorani and Schurr, 2013), 种子形态(Fahlgren et al., 2015)等
		近红外成像	灰度图像	900-1700 nm	NIR反射值, 组织含水量		含水量垂直分布, 辐射分布等		小麦叶片含水量监测(Elsayed et al., 2011), 玉米木质素(Hatfield and Fukushima, 2005), 叶片含水量(Seelig et al., 2008), 大麦抗旱性状等
		热成像	灰度图像, IR反射值	8000-14000 nm	IR反射值, 叶片或冠层温度		冠层温度下降差, 温度分布		甘蔗产量(Basnayake et al., 2017), 大麦及小麦叶片水分状态(Munns et al., 2010), 干旱耐受性评价(Fischer et al., 1998, Tuberosa, 2011, Araus et al., 2012), 产量贡献(Reynolds et al., 1999), 拟南芥突变体筛选(Merlot et al., 2002)等
		荧光成像	颜色图像, 荧光反射值	400-700 nm	荧光反射强度		衰老指数, 胁迫指数等		小麦及大麦干旱胁迫(Munns et al., 2010), 拟南芥干旱胁迫(Woo et al., 2008), 叶片生长及胁迫(Jansen et al., 2009), 除草剂应用(Chaerle et al., 2003), 作物生长发育及胁迫(Chaerle et al., 2009)等
	叶绿素荧光成像		颜色图像	400-700 nm	光合效率, 光系统II产生荧光强度	叶绿素指数, 花青素指数, F_v/F_m, F_o		光合效率检测(van Veelen et al., 2018), 小麦光合动力学变化(Murchie et al., 2018), 拟南芥干旱胁迫(Yao et al., 2018)等
	多光谱成像		灰度或彩色图像, 光谱吸收曲线	400-2500 nm	可溶性固形物, 花青素, 叶绿素含量, 叶片N、P元素含量, 组织含水量等	归一化植被指数(NDVI), 叶黄素, 叶绿素等色素的反射峰值, 生化组分光谱值, 植物光谱反射指数		马铃薯晚疫病分级评价(Franceschini et al., 2019), 油菜光叶斑病(Veys et al., 2019), 葡萄病害分辨(Albetis et al., 2019), 棉花水分利用效率(Thorp et al., 2018)等
	高光谱成像		灰度或彩色图像, 光谱吸收曲线	400-2500 nm连续波长	可溶性固形物, 花青素, 叶绿素含量, 叶片N、P元素含量, 组织含水量等	归一化植被指数(NDVI), 叶黄素, 叶绿素等色素的反射峰值, 叶片组织反射率, 叶片生化组分光谱值, 植物光谱反射指数(NDVI、RVI和GVI等)		小麦光合效率评估(EI-Hendawy et al., 2019), 卷心莴苣评分及分类(Bauer et al., 2011), 葡萄浆果品质(Gutiérrez et al., 2019), 小麦N素评估(Elsayed et al., 2018), 小麦含氮量监测(Camino et al., 2018), 小麦耐盐(Moghimi et al., 2018)等
三维成像技术	激光雷达成像		点阵云图	532 nm	株高, 叶面积, 物候学信息, 叶型, 根系结构, 产量性状, 穗型, 种子形态等	大小(高度、宽度、长度), 倾角如叶倾角(点云倾角), 基本体积测量等		高粱田间穗数及尺寸(Malambo et al., 2019), 粗肋草属叶片性状分析(Panjvani et al., 2019), 玉米田间结构特征(Bao et al., 2019)等
	计算机断层扫描成像		连续灰度图像	100 µm或更低	生物量, 分蘖数、分蘖角度, 穗粒数, 内部结构信息等	各部位密度分布, 茎秆强度等		玉米茎秆抗倒伏(Zhang et al., 2018), 高粱茎解剖学特征(Gomez et al., 2018), 小麦根系研究(Douarre et al., 2018, Xu et al., 2018), 根系分泌物(Valdes et al., 2018)等
	磁共振成像		连续灰度图像	200-500 µm	根系长度, 体内可动水的分布图, 内部结构等	组织体内电磁分布, 根系结构等		根系分泌物(van Veelen et al., 2018), 根系生长(Poorter et al., 2012), 鹰嘴豆表型(Rascher et al., 2011)等

分类		成像技术	元数据	波长范围	采集性状 (传统农艺性状)		新参数		应用实例
二维成像技术		可见光成像	灰度或彩色图像, RGB通道反射值	400-700 nm	株高, 叶面积, 物候学信息, 叶型, 根系结构, 产量性状, 穗型, 种子形态, 绝对生长率(GR)和相对生长率(RGR)等		投影面积, 紧密度, 叶片衰老指数、伸长速率、卷曲指数, 叶面积垂直分布, 绿度, 开花率等		玉米耐冷性(Enders et al., 2019), 小麦产量预测(Bowman et al., 2015), 马铃薯出苗量预测(Li et al., 2019), 小麦开花率(Wang et al., 2019), 大麦叶片伸长QTL (Ward et al., 2019), 拟南芥群体生长(Vasseur et al., 2017), 春小麦产量性状(Neilson et al., 2015), 大麦抗旱性状(Chen et al., 2014), 水稻QTL (Yang et al., 2013), C₄作物地上部生物量(Fiorani and Schurr, 2013), 种子形态(Fahlgren et al., 2015)等
		近红外成像	灰度图像	900-1700 nm	NIR反射值, 组织含水量		含水量垂直分布, 辐射分布等		小麦叶片含水量监测(Elsayed et al., 2011), 玉米木质素(Hatfield and Fukushima, 2005), 叶片含水量(Seelig et al., 2008), 大麦抗旱性状等
		热成像	灰度图像, IR反射值	8000-14000 nm	IR反射值, 叶片或冠层温度		冠层温度下降差, 温度分布		甘蔗产量(Basnayake et al., 2017), 大麦及小麦叶片水分状态(Munns et al., 2010), 干旱耐受性评价(Fischer et al., 1998, Tuberosa, 2011, Araus et al., 2012), 产量贡献(Reynolds et al., 1999), 拟南芥突变体筛选(Merlot et al., 2002)等
		荧光成像	颜色图像, 荧光反射值	400-700 nm	荧光反射强度		衰老指数, 胁迫指数等		小麦及大麦干旱胁迫(Munns et al., 2010), 拟南芥干旱胁迫(Woo et al., 2008), 叶片生长及胁迫(Jansen et al., 2009), 除草剂应用(Chaerle et al., 2003), 作物生长发育及胁迫(Chaerle et al., 2009)等
	叶绿素荧光成像		颜色图像	400-700 nm	光合效率, 光系统II产生荧光强度	叶绿素指数, 花青素指数, F_v/F_m, F_o		光合效率检测(van Veelen et al., 2018), 小麦光合动力学变化(Murchie et al., 2018), 拟南芥干旱胁迫(Yao et al., 2018)等
	多光谱成像		灰度或彩色图像, 光谱吸收曲线	400-2500 nm	可溶性固形物, 花青素, 叶绿素含量, 叶片N、P元素含量, 组织含水量等	归一化植被指数(NDVI), 叶黄素, 叶绿素等色素的反射峰值, 生化组分光谱值, 植物光谱反射指数		马铃薯晚疫病分级评价(Franceschini et al., 2019), 油菜光叶斑病(Veys et al., 2019), 葡萄病害分辨(Albetis et al., 2019), 棉花水分利用效率(Thorp et al., 2018)等
	高光谱成像		灰度或彩色图像, 光谱吸收曲线	400-2500 nm连续波长	可溶性固形物, 花青素, 叶绿素含量, 叶片N、P元素含量, 组织含水量等	归一化植被指数(NDVI), 叶黄素, 叶绿素等色素的反射峰值, 叶片组织反射率, 叶片生化组分光谱值, 植物光谱反射指数(NDVI、RVI和GVI等)		小麦光合效率评估(EI-Hendawy et al., 2019), 卷心莴苣评分及分类(Bauer et al., 2011), 葡萄浆果品质(Gutiérrez et al., 2019), 小麦N素评估(Elsayed et al., 2018), 小麦含氮量监测(Camino et al., 2018), 小麦耐盐(Moghimi et al., 2018)等
三维成像技术	激光雷达成像		点阵云图	532 nm	株高, 叶面积, 物候学信息, 叶型, 根系结构, 产量性状, 穗型, 种子形态等	大小(高度、宽度、长度), 倾角如叶倾角(点云倾角), 基本体积测量等		高粱田间穗数及尺寸(Malambo et al., 2019), 粗肋草属叶片性状分析(Panjvani et al., 2019), 玉米田间结构特征(Bao et al., 2019)等
	计算机断层扫描成像		连续灰度图像	100 µm或更低	生物量, 分蘖数、分蘖角度, 穗粒数, 内部结构信息等	各部位密度分布, 茎秆强度等		玉米茎秆抗倒伏(Zhang et al., 2018), 高粱茎解剖学特征(Gomez et al., 2018), 小麦根系研究(Douarre et al., 2018, Xu et al., 2018), 根系分泌物(Valdes et al., 2018)等
	磁共振成像		连续灰度图像	200-500 µm	根系长度, 体内可动水的分布图, 内部结构等	组织体内电磁分布, 根系结构等		根系分泌物(van Veelen et al., 2018), 根系生长(Poorter et al., 2012), 鹰嘴豆表型(Rascher et al., 2011)等